Predictive issue detection

ABSTRACT

A device may receive data that includes invoice data related to historical invoices from an organization, contact data related to historical contacts between the organization and various entities, and dispute data related to historical disputes between the organization and the various entities. The device may determine a profile for the data. The device may determine a set of supervised learning models for the historical invoices based on one or more of the historical contacts, the historical disputes, the historical invoices, or historical patterns related to the historical invoices. The device may determine, using the profile, a set of unsupervised learning models for the historical invoices independent of the one or more of the historical contacts, the historical disputes, or the historical patterns. The device may determine, utilizing a super model, a prediction for the invoice after the super model is trained. The device may perform one or more actions.

BACKGROUND

An organization generates invoices related to goods and/or servicesoffered by the organization. An invoice includes information thatidentifies the organization, an amount to be paid for the goods and/orservices, a recipient of the goods and/or services, a location at whichthe goods and/or services were provisioned, and/or the like. Paymentterms specify an amount of time that the recipient has to providepayment for the goods and/or services and are identified in the invoice.

SUMMARY

According to some possible implementations, a method may comprise:receiving, by a device, data that includes: invoice data related tohistorical invoices from an organization, contact data related tohistorical contacts between the organization and various entities forthe historical invoices, and dispute data related to historical disputesbetween the organization and the various entities for the historicalinvoices; determining, by the device, a profile for the data afterreceiving the data, wherein the profile includes a set of groupings ofthe data by a set of attributes included in the data; determining, bythe device and using the profile, a set of supervised learning modelsfor the historical invoices based on one or more of: the historicalcontacts, the historical disputes, or historical patterns related to thehistorical invoices, wherein the set of supervised learning models isassociated with training a super model to make a prediction for aninvoice in a context of the one or more of the historical contacts, thehistorical disputes, or the historical patterns; determining, by thedevice and using the profile, a set of unsupervised learning models forthe historical invoices independent of the one or more of the historicalcontacts, the historical disputes, or the historical patterns, whereinthe set of unsupervised learning models is associated with training thesuper model to make the prediction for the invoice independent of thecontext of the one or more of the historical contacts, the historicaldisputes, or the historical patterns; determining, by the device andutilizing the super model, the prediction for the invoice after thesuper model is trained using the set of supervised learning models andthe set of unsupervised learning models, wherein the predictionindicates a likelihood of at least one type of issue being associatedwith the invoice; and performing, by the device, one or more actionsbased on the prediction.

According to some possible implementations, a device may comprise: oneor more memories; and one or more processors, communicatively coupled tothe one or more memories, to: receive data that is related to training asuper model to determine a prediction of at least one type of issuebeing associated with an invoice; determine a profile for the data afterreceiving the data, wherein the profile includes a set of groupings ofthe data by a set of attributes included in the data; determine, usingthe profile, a set of supervised learning models for historical invoicesbased on at least one of: historical patterns related to the historicalinvoices, historical contacts related to historical issues associatedwith the historical invoices, or historical disputes related to thehistorical invoices, wherein the set of supervised learning models isassociated with training the super model to make the prediction for theinvoice in a context of the at least one of the historical patterns, thehistorical contacts, or the historical disputes; determine, using theprofile, a set of unsupervised learning models for the historicalinvoices independent of the at least one of the historical patterns, thehistorical contacts, or the historical disputes, wherein the set ofunsupervised learning models is associated with training the super modelto make the prediction for the invoice independent of the at least oneof the context of the historical patterns, the historical contacts, orthe historical disputes; generate, utilizing the super model, theprediction for the invoice by processing invoice-related data after thesuper model is trained using the set of supervised learning models andthe set of unsupervised learning models, wherein the at least one typeof issue includes at least one of: a late payment associated with theinvoice, the late payment after a particular time, or an amount of timefor the late payment; and rank the invoice relative to one or more otherinvoices based on being predicted to be associated with the at least onetype of issue based on the prediction.

According to some possible implementations, a non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: receive invoice-relateddata that is to be processed by a super model to determine a predictionof at least one type of issue being associated with an invoice, whereinthe super model has been trained on data related to: historical invoicesfrom an organization, historical contacts between the organization andvarious entities for the historical invoices, and historical disputesbetween the organization and the various entities for the historicalinvoices; process, using a set of supervised learning models, theinvoice-related data after receiving the invoice-related data, whereinthe set of supervised learning models has been trained to make theprediction for the invoice in a context of one or more of the historicalcontacts, the historical disputes, or historical patterns; process,using a set of unsupervised learning models, the invoice-related dataafter processing the set of supervised learning models, wherein the setof unsupervised learning models has been trained to make the predictionfor the invoice independent of the context of the one or more of thehistorical patterns, the historical contacts, or the historicaldisputes; process, using the super model, output from the set ofsupervised learning models and the set of unsupervised learning modelsto make the prediction of the at least one type issue being associatedwith the invoice after processing the invoice-related data using the setof supervised learning models and the set of unsupervised learningmodels; and perform one or more actions based on the prediction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1G are diagrams of an example implementation described herein.

FIGS. 2-3 are diagrams of example implementations related to predictiveissue detection.

FIG. 4 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 5 is a diagram of example components of one or more devices of FIG.2.

FIGS. 6-8 are flow charts of example processes for predictive issuedetection.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

An organization can generate hundreds, thousands, or more invoices on adaily, monthly, or yearly basis. For example, the organization maygenerate the invoices for goods and/or services provisioned by theorganization. Timely collection of amounts related to the invoices isneeded to ensure uninterrupted operations of the organization. Withlimited resources available to the organization (e.g., computingresources, human resources, money, and/or the like), the organizationmay want to prioritize collection of unpaid amounts so as to efficientlyuse the limited resources, to maximize a total amount collected, and/orthe like. However, with hundreds, thousands, or more invoices generatedat the rate described above, the amount of data needed to be processedto prioritize the collection for the invoices can easily overloadconventional computing devices (e.g., can consume an excessive amount ofcomputing resources, can cause a conventional computing device to crash,and/or the like), can take an excessive amount of time using theconventional computing resources (e.g., delays due to use of theconventional computing resources for other tasks), and/or the like,thereby making prioritization of the invoices and/or predictions relatedto the invoices difficult, if not impossible.

Some implementations described herein provide an invoice analysisplatform that is capable of processing data related to hundreds,thousands, or more invoices to predict which invoices are likely to beassociated with an issue, to prioritize collection of unpaid amountsassociated with the invoices, to generate a collection plan forcollecting the unpaid amounts, and/or the like in real-time or nearreal-time (e.g., as the invoices are generated). In this way, theinvoice analysis platform provides a tool that facilitates quickprediction related to the invoices and/or prioritization of theinvoices, pre-staging and/or efficient deployment of resourcesassociated with an organization related to collecting the unpaidamounts, and/or the like. Utilizing the invoice analysis platform inthis manner reduces an amount of time needed to make predictions and/ordetermine prioritizations for the invoices. In addition, utilizing theinvoice analysis platform in this manner conserves computing resourcesthat would otherwise be wasted attempting to make the predictions and/orto determine the prioritizations using conventional computing devices.Further, using the invoice analysis platform in this manner conservesresources associated with an organization that would otherwise be wastedvia a less efficient deployment of the resources. Further, the invoiceanalysis platform can improve an accuracy of prioritization and/orcollection of invoices, thereby conserving time, computing resources,and/or the like that would otherwise be consumed relative to not usingthe invoice analysis platform.

Further, in this way, the invoice analysis platform removes humansubjectivity and waste from determining prioritizations for invoices,and may improve speed and efficiency of the process and conservecomputing resources (e.g., processor resources, memory resources, and/orthe like). Furthermore, implementations described herein use a rigorous,computerized process to perform tasks or roles that were not previouslyperformed, thereby providing a new tool for analysis of invoicesgenerated by an organization. Further, a process for determining aprediction and/or a prioritization conserves computing resources (e.g.,processor resources, memory resources, and/or the like) of a device thatwould otherwise be wasted in attempting to use another technique toprocess data related to an invoice generated by the organization and/orto inefficiently collect an unpaid amount associated with the invoice.

FIGS. 1A-1G are diagrams of an example implementation 100 describedherein. As shown in FIG. 1A, implementation 100 includes a serverdevice, a client device, a user device, and an invoice analysisplatform.

As shown by reference number 105, the invoice analysis platform mayreceive data, such as invoice data, contact data, dispute data, and/orthe like. For example, the invoice data may be related to historicalinvoices generated by an organization. Continuing with the previousexample, the invoice data may identify the historical invoices,respective amounts for the historical invoices, respective goods and/orservices associated with the historical invoices, respective recipientsof the goods and/or services, respective locations of the respectiverecipients and/or to which the respective goods and/or services wereprovided, respective issue dates for the historical invoices, respectivepayment dates for the historical invoices, whether the historicalinvoices were overdue at any time, and/or the like. Additionally, oralternatively, and as another example, the contact data may be relatedto historical contacts between the organization and various entities forthe historical invoices. Continuing with the previous example, thecontact data may identify respective dates and/or types of contacts(e.g., a telephone call, an email, a text, a letter, a web portalnotification, an in-person visit, and/or the like) between entities(e.g., organizations, individuals, government entities, and/or the like)associated with a historical invoice, whether the historical contactswere successful in resulting in payment of the respective amounts, aquantity of contacts between the entities, and/or the like.

Additionally, or alternatively, and as another example, the dispute datamay be related to historical disputes between entities associated with ahistorical invoice. Continuing with the previous example, the disputedata may identify whether any of the historical invoices were disputed,aspects of an invoice that were (or are being) disputed, whether thedispute was resolved, a date on which the dispute was resolved, and/orthe like.

Additionally, or alternatively, the invoice analysis platform mayreceive data that identifies historical patterns of issues that werepredicted to be associated with the historical invoices, whether thehistorical patterns were accurate, and/or the like. For example, theinvoice analysis platform may receive data that identifies historicalpatterns as to whether particular historical invoices were to be paidon-time, a quantity of days, weeks, or months that payments forhistorical invoices were expected to be outstanding, collection actionsthat were predicted to be successful for collecting payments forhistorical invoices, and/or the like. In some implementations, theinvoice analysis platform may receive the data from the server device,the client device, and/or the user device. In some implementations, theinvoice analysis platform may receive the data based on requesting thedata, according to a schedule, periodically, and/or the like.Additionally, or alternatively, the invoice analysis platform mayreceive data related to characteristics and/or demographics of anentity, such as a size of an organization, an income level of anindividual, a gender of an individual, an age of an individual, and/orthe like.

In some implementations, the invoice analysis platform may receive thedata in various forms. For example, the invoice analysis platform mayreceive the data in the form of an image (e.g., an image of an invoice,an image of a document associated with a contact and/or a dispute,and/or the like), as text (e.g., as text of an invoice from an invoicingsystem, text of a document related to a contact and/or a dispute, and/orthe like), as application data from an application hosted on, executedon, and/or the like the server device, the client device, and/or theuser device, as input to the invoice analysis platform (e.g., via a userinterface associated with the invoice analysis platform), as tabulardata (e.g., in the form of a spread sheet file, a comma-separated values(CSV) file, and/or the like), and/or the like. In some implementations,when receiving the data, the invoice analysis platform may receivethousands, millions, or more data elements for thousands, millions, ormore invoices. In this way, the invoice analysis platform may process adata set that may overload conventional computing resources.

Turning to FIG. 1B, and as shown by reference number 110, the invoiceanalysis platform may determine a profile for the data. For example, theinvoice analysis platform may determine the profile for the data afterreceiving the data. In some implementations, a profile may include a setof groupings of the data by a set of attributes included in the data.For example, the profile may organize data related to historicalinvoices, historical contacts, historical disputes, historical patterns,and/or the like by entity (e.g., by entity that provisioned goods and/orservices related to a historical invoice, by entity that received thegoods and/or services, and/or the like), location (e.g., by location ofan entity associated with a historical invoice, by location to whichgoods and/or services associated with a historical invoice wereprovided, and/or the like), by goods and/or services associated with thehistorical invoices, and/or the like. Continuing with the previousexample, a profile for a recipient of goods and/or services may includedata related to historical invoices associated with the recipient,historical contacts related to collecting payments for the historicalinvoices associated with the recipient, historical disputes between therecipient and organizations that generated the historical invoices,and/or the like. Additionally, or alternatively, the invoice analysisplatform may organize the data by an amount associated with historicalinvoices (e.g., amounts to be paid for goods and/or services associatedwith the historical invoices, amounts by which the historical invoiceswere underpaid, and/or the like).

In some implementations, the invoice analysis platform may organize thedata for the profile based on unique identifiers included in the data(e.g., unique identifiers that uniquely identify an entity associatedwith the data, a location associated with the data, an invoiceassociated with the data, and/or the like). In some implementations, theunique identifiers may be included in the data as attributes of the data(e.g., as a field with a unique value, such as a name, an identificationnumber, and/or the like), and the invoice analysis platform may organizethe data based on the unique identifiers included as the attributes inthe data.

Additionally, or alternatively, the invoice analysis platform mayprocess the data to identify the unique identifiers. For example, theinvoice analysis platform may process images using an image processingtechnique, such as a computer vision technique, a feature detectiontechnique, an optical character recognition (OCR) technique, and/or thelike to identify an alphanumeric string, a symbol, a code (e.g., abarcode, a matrix barcode, and/or the like) in the image (e.g., thatidentify the presence of a unique identifier, that are a uniqueidentifier, and/or the like). Continuing with the previous example, theinvoice analysis platform may compare the alphanumeric string, thesymbol, the code, and/or the like to information stored in a datastructure and/or in memory resources of the invoice analysis platform todetermine which unique identifiers are included in the image.

Additionally, or alternatively, and as another example, the invoiceanalysis platform may process the data using a text processingtechnique, such as a natural language processing technique, a textanalysis technique, and/or the like. Continuing with the previousexample, the invoice analysis platform may process the text to identifyan alphanumeric string, a symbol, a code, and/or the like included inthe data (e.g., that indicate a presence of a unique identifier, thatare a unique identifier, and/or the like), and may identify the uniqueidentifiers included in the text in a manner similar to that describedabove.

Additionally, or alternatively, and as another example, the invoiceanalysis platform may process the data using a model (e.g., a machinelearning model, an artificial intelligence model, and/or the like) toidentify a unique identifier included in the data. For example, theinvoice analysis platform may use the model to process an image and/ortext to identify an alphanumeric string, a symbol, a code, and/or thelike included in the data, to identify an area of the data (e.g., anarea of an image and/or text) that likely includes a unique identifier,and/or the like (e.g., based on having been trained to identify uniqueidentifiers in the data, a likely area in the data that may include aunique identifier, and/or the like). In some implementations, the modeland/or training of the model may be similar to that described elsewhereherein.

Reference number 115 shows example profiles that the invoice analysisplatform may generate. As shown, a profile may organize the data thatthe invoice analysis platform received by customer, by invoice, and/orthe like. In this way, a profile facilitates quick and easy access todata in an organized manner. This conserves processing resources of theinvoice analysis platform relative to not using a profile, facilitatestraining of a model to identify issues in an invoice based on attributesincluded in the data (e.g., the invoice analysis platform may train themodel on a particular customer or customers generally, on a particularinvoice or invoices generally, and/or the like), thereby improving anaccuracy of the model with regard to predicting issues that may occurwith invoices.

Turning to FIG. 1C, and as shown by reference number 120, the invoiceanalysis platform may determine a set of supervised learning models forthe data. For example, the invoice analysis platform may determine theset of supervised learning models after determining the profile. In someimplementations, a supervised learning model may include a modeltrained, via supervised learning, on features of the data related topredicting an issue for an invoice. For example, a supervised learningmodel may be trained on a pattern of invoices for an entity, a location,and/or the like (e.g., amounts of the invoices, amounts of days theinvoices were (or are) outstanding, quantities and/or types of contactsrelated to the invoices, disputes related to the invoices, and/or thelike). In some implementations, the set of supervised learning modelsmay be used to make a prediction for an invoice in a context ofhistorical contacts, historical disputes, historical invoices,historical patterns, and/or the like. For example, the set of supervisedlearning models may be trained to predict whether an invoice will bepaid on-time, a quantity of days the invoice will be outstanding, typesof collection actions that are likely to result in collection of anunpaid amount associated with the invoice, and/or the like.

As shown by reference number 125, the invoice analysis platform mayinput data related to the historical invoices, the historical disputes,the historical contacts, and/or the historical patterns into a machinelearning model to determine the set of supervised learning models. Forexample, the invoice analysis platform may input the data related to thehistorical invoices, the historical disputes, the historical contacts,and/or the historical patterns to train the machine learning model(e.g., to generate the set of supervised machine learning models).

In some implementations, when processing the data related to thehistorical invoices, the historical disputes, the historical contacts,and/or the historical patterns, the machine learning model may group thehistorical invoices, the historical disputes, and/or the historicalcontacts by historical patterns. For example, the machine learning modelmay group, utilizing the data related to the historical invoices, thehistorical disputes, and/or the historical contacts, the data based onwhether the invoice analysis platform accurately made historicalpatterns for the historical invoices, the historical disputes, and/orthe historical contacts.

In some implementations, the invoice analysis platform may use theprofile for the data as the input to the machine learning model. Thisfacilitates generation of the set of supervised learning models fordifferent attributes included in the data, which can make the supervisedlearning models more dynamic, can improve an accuracy of the set ofsupervised learning models, and/or the like.

In some implementations, prior to inputting the data related to thehistorical invoices, the historical disputes, and/or the historicalcontacts, the invoice analysis platform may prepare and/or pre-processthe data. For example, the invoice analysis platform may identifykeywords included in the data, such as unique identifiers that arecommon across the data related to the historical invoices, thehistorical disputes, the historical patterns, and/or the historicalcontacts, terms that identify accurate historical patterns, terms thatidentify inaccurate historical patterns, amounts associated with ahistorical invoice, a historical contact, a historical dispute, and/or ahistorical pattern, locations associated with the historical invoices,the historical contacts, and/or the historical disputes, and/or thelike. Additionally, or alternatively, the invoice analysis platform mayremove leading and/or trailing spaces from text included in the datarelated to the historical invoices, the historical disputes, thehistorical patterns, and/or the historical contacts, may removenon-American Standard Code for Information Interchange (ASCII)characters from the data, and/or the like. This facilitates quick and/oreasy processing of the data related to the historical invoices, thehistorical disputes, the historical patterns, and/or the historicalcontacts by making the data more uniform, thereby facilitating fastdetermination of the set of supervised learning models, more accuratedetermination of the set of supervised learning models, and/or the like.

In some implementations, the invoice analysis platform may generate theset of supervised learning models by training the machine learningmodel. For example, the invoice analysis platform may train the machinelearning model to generate the set of supervised learning models fromthe data related to the historical invoices, the historical disputes,the historical contacts, and/or the historical patterns.

In some implementations, the invoice analysis platform may train themachine learning model on a training set of data. For example, thetraining set of data may include data related to historical invoices,historical contacts, and historical disputes, and data that identifieshistorical patterns related to the historical invoices, the historicaldisputes, and/or the historical contacts. Additionally, oralternatively, when the invoice analysis platform inputs the datarelated to the historical invoices, the historical disputes, thehistorical contacts, and/or the historical patterns into the machinelearning model, the invoice analysis platform may input a first portionof the data as a training set of data, a second portion of the data as avalidation set of data, and third portion of the data as a test set ofdata (e.g., to be used to determine the set of supervised learningmodels). In some implementations, the invoice analysis platform mayperform multiple iterations of training of the machine learning model,depending on an outcome of testing of the machine learning model (e.g.,by submitting different portions of the data as the training set ofdata, the validation set of data, and the test set of data).

In some implementations, when training the machine learning model, theinvoice analysis platform may utilize a random forest classifiertechnique to train the machine learning model. For example, the invoiceanalysis platform may utilize a random forest classifier technique toconstruct multiple decision trees during training and may output aclassification of data. Additionally, or alternatively, when trainingthe machine learning model, the invoice analysis platform may utilizeone or more gradient boosting techniques to generate the machinelearning model. For example, the invoice analysis platform may utilizean xgboost classifier technique to generate a prediction model from aset of weak prediction models.

In some implementations, when training the machine learning model, theinvoice analysis platform may utilize logistic regression to train themachine learning model. For example, the invoice analysis platform mayutilize a binary classification of the data related to the historicalinvoices, the historical disputes, the historical contacts, and/or thehistorical patterns (e.g., whether the historical invoices, thehistorical disputes and/or the historical contacts match the historicalpatterns) to train the machine learning model to determine the set ofsupervised learning models based on the classification of the data.Additionally, or alternatively, when training the machine learningmodel, the invoice analysis platform may utilize a Naive Bayesclassifier to train the machine learning model. For example, the invoiceanalysis platform may utilize binary recursive partitioning to dividethe data related to historical invoices, the historical disputes, thehistorical contacts, and/or the historical patterns into various binarycategories (e.g., starting with an accurate or inaccurate binarycategory for historical patterns related to the historical invoices, thehistorical disputes, and/or the historical contacts). Based on usingrecursive partitioning, the invoice analysis platform may reduceutilization of computing resources relative to manual, linear sortingand analysis of data points, thereby enabling use of thousands,millions, or billions of data points to train a machine learning model,which may result in a more accurate machine learning model than usingfewer data points.

Additionally, or alternatively, when training the machine learningmodel, the invoice analysis platform may utilize a support vectormachine (SVM) classifier. For example, the invoice analysis platform mayutilize a linear model to implement non-linear class boundaries, such asvia a max margin hyperplane. Additionally, or alternatively, whenutilizing the SVM classifier, the invoice analysis platform may utilizea binary classifier to perform a multi-class classification. Use of anSVM classifier may reduce or eliminate overfitting, may increase arobustness of the machine learning model to noise, and/or the like.

In some implementations, the invoice analysis platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert. In some implementations, the invoice analysis platformmay use one or more other model training techniques, such as a neuralnetwork technique, a latent semantic indexing technique, and/or thelike. For example, the invoice analysis platform may perform amulti-layer artificial neural network processing technique (e.g., usinga two-layer feedforward neural network architecture, a three-layerfeedforward neural network architecture, and/or the like) to performpattern recognition with regard to patterns of supervised learningmodels, patterns of supervised learning models based on an accuracy of ahistorical patterns, and/or the like. In this case, using the artificialneural network processing technique may improve an accuracy of asupervised learning model generated by the invoice analysis platform bybeing more robust to noisy, imprecise, or incomplete data, and byenabling the invoice analysis platform to detect patterns and/or trendsundetectable to human analysts or systems using less complex techniques.

As an example, the invoice analysis platform may use a supervisedmulti-label classification technique to train the machine learningmodel. For example, as a first step, the invoice analysis platform maymap data associated with the historical invoices, the historicaldisputes, the historical contacts, and/or the historical patterns to aset of previously generated supervised learning models after labelingthe historical invoices, the historical disputes, the historicalcontacts, and/or the historical patterns. In this case, the historicalinvoices, the historical disputes, and/or the historical contacts may becharacterized as having been accurately or inaccurately predicted, thehistorical patterns may be characterized as having been accurate orinaccurate, and/or the like (e.g., by a technician, thereby reducingprocessing relative to the invoice analysis platform being required toanalyze each historical invoice, historical contact, historical dispute,and/or historical pattern). As a second step, the invoice analysisplatform may determine classifier chains, whereby labels of targetvariables may be correlated (e.g., in this example, labels may be aresult of a historical pattern and correlation may refer to supervisedlearning models common to the different labels, and/or the like). Inthis case, the invoice analysis platform may use an output of a firstlabel as an input for a second label (as well as one or more inputfeatures, which may be other data relating to the historical invoices,the historical disputes, the historical contacts, and/or the historicalpatterns), and may determine a likelihood that a particular historicalinvoice is to be associated with at least one type of issue and/or isassociated with a set of supervised learning models based on asimilarity to other historical invoices that include similar data. Inthis way, the invoice analysis platform transforms classification from amultilabel-classification problem to multiple single-classificationproblems, thereby reducing processing utilization. As a third step, theinvoice analysis platform may determine a Hamming Loss Metric relatingto an accuracy of a label in performing a classification by using thevalidation set of the data (e.g., an accuracy with which a weighting isapplied to each historical invoice, historical dispute, historicalcontact, and/or historical pattern and whether each historical invoice,historical contact, and/or historical dispute is associated with anissue, results in a correct historical pattern, and/or the like, therebyaccounting for variations among historical invoices, historicalcontacts, and/or historical disputes). As a fourth step, the invoiceanalysis platform may finalize the machine learning model based onlabels that satisfy a threshold accuracy associated with the HammingLoss Metric, and may use the machine learning model for subsequentdetermination of supervised learning models.

Turning to FIG. 1D, and as shown by reference number 130, the set ofsupervised learning models may include various types of models. Forexample, the set of supervised learning models may include a neuralnetwork, where nodes of the neural network are trained and are used toprocess data related to an invoice to make a prediction related to theinvoice. Additionally, or alternatively, and as another example, the setof supervised learning models may include a linear regression model. Forexample, the linear regression model may be trained on an assumption ofa linear relationship between two variables. Additionally, oralternatively, and as another example, the set of supervised learningmodels may include an ensemble-based model. For example, theensemble-based model may combine multiple other models described hereininto a single model.

In some implementations, output from the set of supervised learningmodels may identify a likelihood that an invoice is to be associatedwith an issue, a type of the issue predicted to be associated with theinvoice, and/or the like. Continuing with the previous example, the setof supervised learning models may indicate that historical invoices froma particular country are associated with a high likelihood of needingparticular types of contacts to remedy underpayments related to thehistorical invoices, a quantity of days that historical invoices wereoutstanding, and/or the like. Additionally, or alternatively, and asanother example, the invoice analysis platform may determine a patternof on-time and overdue historical invoices by attribute included in thedata. Continuing with the previous example, the invoice analysisplatform may determine that a first pattern of data related tohistorical invoices, historical contacts, and/or historical disputes(e.g., a pattern of amounts, locations, dates, and/or the like) for anentity is associated with a low likelihood of being associated with anissue, may determine that a second pattern of data related to historicalinvoices, historical contacts, and/or historical disputes for the entityis associated with a high likelihood of being associated with an issue(e.g., a pattern that deviates from the first pattern), and/or the like.

Turning to FIG. 1E, as shown by reference number 135, the invoiceanalysis platform may determine a set of unsupervised learning modelsfor the data. For example, the invoice analysis platform may determinethe set of unsupervised learning models in a manner that is the same asor similar to that described elsewhere herein with regard to the set ofsupervised learning models. In some implementations, the invoiceanalysis platform may determine the set of unsupervised learning modelsindependent of the historical contacts, the historical disputes, and/orthe historical patterns. For example, the invoice analysis platform maybe trained on patterns of data related to the historical invoiceswithout regard to whether the historical invoices are associated withhistorical contacts, historical disputes, accurate or inaccuratehistorical patterns, and/or the like. In some implementations, the setof unsupervised learning models may be used to make predictions for aninvoice without the context of the historical contacts, the historicaldisputes, and/or the historical patterns (e.g., without categorizationof the invoice as having similar patterns of data to a historicalinvoice that is associated with a set of historical contacts, a set ofhistorical disputes, a set of accurate or inaccurate historicalpatterns, and/or the like). In some implementations, the set ofunsupervised learning models may be associated with training a supermodel to make predictions independent of the context of the historicalcontacts, the historical disputes, and/or the historical patterns. As aresult, the set of unsupervised learning models may identify new,unusual, and/or abnormal behavior and/or patterns in the historicalinvoices, which were missed by the historical patterns, that were notassociated with historical disputes and/or historical contacts, and/orthe like.

As shown by reference number 140, the invoice analysis platform maydetermine the set of unsupervised learning models by processing datarelated to historical invoices using a machine learning model. In someimplementations, the machine learning model may be similar to thatdescribed elsewhere herein. For example, the machine learning model mayoutput a set of unsupervised learning models determined from the datarelated to the historical invoices. In some implementations, the invoiceanalysis platform may input the profile for the data related to thehistorical invoices to the machine learning model to determine the setof unsupervised learning models, in a manner that is the same as orsimilar to that described elsewhere herein with regard to the set ofsupervised learning models.

Turning to FIG. 1F, and as shown by reference number 145, the set ofunsupervised learning models may include various types of models. Forexample, the set of unsupervised learning models may include a k-meansclustering model where invoice data is partitioned into clusters with anearest mean. Additionally, or alternatively, and as another example,the set of unsupervised learning models may include an isolation forestwhere decision trees of the isolation forest are generated from theinvoice. Additionally, or alternatively, and as another example, the setof unsupervised learning models may include a kernel density estimation(KDE) model. For example, the KDE model may use a kernel parameter and akernel bandwidth parameter to process data related to an invoice to makea prediction related to the invoice. In this way, the set ofunsupervised learning models may be trained to measure similaritiesbetween data related to the historical invoices and data related to theinvoice and/or to measure similarities between customers behaviors.

Turning to FIG. 1G, and as shown by reference number 150, the invoiceanalysis platform may combine the set of supervised learning models andthe set of unsupervised learning models into a super model. For example,the invoice analysis platform may combine the set of supervised learningmodels and the set of unsupervised learning models into the super modelafter determining the set of supervised learning models and the set ofunsupervised learning models. In some implementations, the invoiceanalysis platform may generate the super model by combining varioustrained models into a single model. In some implementations, the supermodel may include a machine learning model similar to that describedelsewhere herein. In some implementations, and as described elsewhereherein, output from the super model may include a prediction for aninvoice, a score that indicates a likelihood that an invoice will beassociated with an issue, a score that indicates a confidence levelassociated with a prediction, and/or the like.

In some implementations, the super model may include a random forestmodel. For example, the invoice analysis platform may combine the set ofsupervised learning models and the set of unsupervised learning modelsinto a random forest model that can be used to determine a predictionfor an invoice (e.g., by constructing multiple decision trees from theset of supervised learning models and the set of unsupervised learningmodels). Additionally, or alternatively, the super model may include aneural network. For example, the set of supervised learning models andthe set of unsupervised learning models may be combined into a neuralnetwork that can be used to determine a prediction for an invoice (e.g.,where nodes of the neural network are trained to process data related toan invoice in a manner similar to the set of unsupervised learningmodels and/or the set of supervised learning models).

As shown by reference number 155, the invoice analysis platform mayreceive an invoice for which a prediction is to be made. For example,the invoice analysis platform may receive the invoice after generatingthe super model. In some implementations, the invoice may include datathat identifies a set of entities associated with the invoice, an amountassociated with the invoice, a due date associated with the invoice, alocation associated with the invoice, a type of goods and/or servicesassociated with the invoice, and/or the like. In some implementations,the invoice may be similar to a historical invoice described elsewhereherein.

In some implementations, the invoice analysis platform may receive theinvoice from the server device, the client device, the user device,and/or the like. In some implementations, the invoice analysis platformmay receive the invoice when the invoice is generated (e.g., inreal-time or near real-time), may receive a batch of invoices at aparticular time or after a threshold quantity of invoices have beengenerated, and/or the like. In some implementations, the invoiceanalysis platform may receive thousands, millions, or more invoicesassociated with thousands, millions, or more entities, locations, and/orthe like. In this way, the invoice analysis platform may receive aquantity of invoices that cannot be processed manually or objectively(e.g., in a consistent manner) by a human actor.

As shown by reference number 160, the invoice analysis platform maydetermine the prediction for the invoice. For example, the invoiceanalysis platform may determine the prediction for the invoice afterreceiving the invoice. In some implementations, the invoice analysisplatform may determine the prediction by processing invoice-related data(e.g., data included in the invoice). For example, the invoice analysisplatform may process data identifying entities associated with theinvoice, an amount of a payment associated with the invoice, a locationof the entities, goods, and/or services associated with the invoice,and/or the like using the set of supervised learning models, the set ofunsupervised learning models, and/or the super model to determine aprediction for the invoice. In some implementations, a prediction mayinclude a prediction of the invoice being associated with a type ofissue, such as a late payment associated with the invoice, a latepayment after a particular time, an amount of time for the late payment,whether the invoice is likely to be disputed, types of contacts thatwill be needed and/or that will be successful in collecting any latepayments associated with the invoice (e.g., an email, a text, atelephone call, an in-person visit from a collection agent and/or thelike), and/or the like.

In some implementations, the invoice analysis platform may determine ascore for the invoice and may determine a prediction for the invoicebased on the score. For example, the invoice analysis platform maydetermine the score after receiving the invoice by processinginvoice-related data using the set of supervised learning models, theset of unsupervised learning models, and/or the super model. In someimplementations, the score may indicate a likelihood of the invoiceincluding a type of issue. For example, the score may indicate alikelihood of the invoice including a type of issue similar to thatdescribed above. In some implementations, a score that satisfies athreshold may indicate a particular type of issue, and the invoiceanalysis platform may determine a prediction based on the scoresatisfying the threshold. Additionally, or alternatively, the invoiceanalysis platform may determine different scores for different types ofissues, and may determine a set of predictions for the different typesof issues based on the different scores. Additionally, or alternatively,the invoice analysis platform may determine a score that indicates aconfidence level of a prediction. For example, a score output from thesuper model may indicate a confidence level of a prediction based on asimilarity between attributes associated with the invoice and attributesof one or more historical invoices.

In some implementations, the invoice analysis platform may supplementdata from an invoice with data related to entities associated with theinvoice, such as demographic data. In some implementations, the invoiceanalysis platform may determine a prediction, a score, and/or the likebased on the data related to the entities, thereby accounting forvariations in characteristics of entities.

As shown by reference number 165, the invoice analysis platform mayinput the invoice (or data extracted from the invoice using a textprocessing technique, an image processing technique, and/or the likesimilar to that described elsewhere herein) into the set of supervisedlearning models and/or the set of unsupervised learning models. Inaddition, the invoice analysis platform may input other data inassociation with inputting the invoice, such as data associated withentities associated with the invoice, data related to a contact betweenentities, data related to a dispute between entities, and/or the like.

In some implementations, the set of supervised learning models mayidentify patterns in the data extracted from the invoice or other datain a context of the historical invoices, the historical patterns, thehistorical contacts, and/or the historical disputes. In someimplementations, output from the set of supervised learning models mayinclude a score that indicates a similarity of the invoice to ahistorical invoice, a likelihood of the invoice being associated with adispute similar to a historical dispute, a likelihood of the invoiceneeding a contact similar to a historical contact, a likelihood of atype of issue occurring for the invoice, a similarity of a contact to ahistorical contact, a similarity of a dispute to a historical dispute,and/or the like. Additionally, or alternatively, the output may identifyan accuracy of historical patterns related to the historical invoice,the historical dispute, the historical contact, historical issues,and/or the like.

In some implementations, the set of unsupervised learning models mayidentify patterns in the extracted data independent of a context of thehistorical invoices, the historical contacts, the historical disputes,and/or the historical patterns. For example, the set of unsupervisedlearning models may identify particular and/or abnormal patterns in theextracted data, and may output information that identifies theparticular and/or abnormal patterns. In some implementations, the outputfrom the set of unsupervised learning models may include a score thatidentifies whether the patterns of data match that for the same entity,the same goods and/or services, and/or the like.

In some implementations, the invoice analysis platform may processoutput from the set of supervised learning models and/or the set ofunsupervised learning models using the super model. For example, theinvoice analysis platform may input scores, patterns of data,invoice-related data, contact-related data, dispute-related data, and/orthe like into the super model in association with determining theprediction for the invoice. In some implementations, the invoiceanalysis platform may use the super model to determine the prediction, ascore, and/or the like. Continuing with the previous example, theinvoice analysis platform may determine whether a combination oflocation, amount, entity, and/or the like associated with the invoice,the contact, and/or the dispute matches a pattern of historicalinvoices, historical contacts, historical disputes, historical patterns,and/or the like on which the set of supervised learning models and/orthe set of unsupervised learning models were trained.

Additionally, or alternatively, the super model may analyze scores fromthe set of supervised learning models and/or the set of unsupervisedlearning models to determine a total score for the invoice. For example,the total score may be a contextualization of scores from the set ofsupervised learning models with scores from the set of unsupervisedlearning models, such as to adjust scores from the set of supervisedlearning models based on unique patterns of data for the invoice, thecontact, and/or the dispute, to contextualize scores related to theunique patterns of data of the invoice to historical invoices,historical contacts, historical disputes, and/or the like. In this way,the super model may utilize output from both the set of supervisedlearning models and the set of unsupervised learning models to determinea prediction.

In some implementations, the super model may output a prediction basedon the total score satisfying a threshold. Additionally, oralternatively, the super model may output a score and the invoiceanalysis platform may determine the prediction based on the score. Forexample, the super model may output different scores for different typesof issues occurring (e.g., that indicate likelihoods of the differenttypes of issues occurring), for different contacts (e.g., that indicatelikelihoods of the different types of contacts being successful incollecting an underpayment), that indicate a likelihood of the invoicebeing paid on-time or by a particular time, and/or the like, and theinvoice analysis platform may determine various predictions based onthese different scores.

As shown by reference number 170, the super model may output theprediction (or a score) after the invoice analysis platform hasprocessed the invoice-related data using the super model. In someimplementations, the prediction may be associated with an indication ofa likelihood of the prediction occurring, a confidence level associatedwith the prediction, and/or the like. In some implementations, theprediction may be associated with a score (e.g., that indicates thelikelihood of the prediction occurring, the confidence level of theprediction occurring, and/or the like). For example, the score may be anaverage score, a range of scores, and/or the like. Continuing with theprevious example, the invoice analysis platform may perform multipleiterations of processing the invoice-related data and may generate thescore based on the scores associated with the multiple iterations.

As shown by reference number 175, the invoice analysis platform mayperform an action based on the prediction (or a score associated withthe prediction). For example, the invoice analysis platform may performan action after determining the prediction for the invoice. In someimplementations, the invoice analysis platform may trigger an alarmbased on the prediction (e.g., based on a type of issue associated withthe prediction). Additionally, or alternatively, the invoice analysisplatform may send a message to the client device, the user device,and/or the server device (e.g., that includes information thatidentifies the invoice, the prediction, and/or the like). Additionally,or alternatively, the invoice analysis platform may generate a reportthat identifies the prediction (e.g., that includes information thatidentifies the prediction, a likelihood of the prediction occurring,and/or the like), and may store the report in the server device and/ormay output the report via the client device and/or the user device(e.g., via a display).

In some implementations, the invoice analysis platform may determine apriority for the invoice. For example, the invoice analysis platform maydetermine a priority for the invoice relative to one or more otherinvoices. Continuing with the previous example, the invoice analysisplatform may determine the priority based on a severity of a type ofissue associated with the invoice relative to other types of issuesassociated with the one or more other invoices, based on a likelihood ofa prediction associated with the invoice relative to other likelihoodsof other predictions associated with the one or more other invoices,and/or the like. In some implementations, the invoice analysis platformmay generate a collection plan for the invoice based on a priority forthe invoice, a type of issue predicted to occur with respect to theinvoice, and/or the like. For example, the invoice analysis platform maygenerate a collection plan that identifies types of actions to beperformed with respect to collecting payment on an invoice, times atwhich to perform the set of actions, an order in which to perform theset of actions, and/or the like. Continuing with the previous example,the set of actions may include sending a message to another deviceassociated with an entity associated with the invoice, placing a robotictelephone call to a telephone associated with the entity, populating anelectronic queue associated with a collection agent with informationrelated to the invoice, dispatching the collection agent to a locationassociated with the entity, and/or the like.

In some implementations, the invoice analysis platform may determinethat an invoice is outstanding at a particular time, such as a due datefor a payment associated with the invoice. For example, the invoiceanalysis platform may store information that identifies the due date ina data structure and may determine that the invoice is outstanding whenthe due date elapses without receiving an indication that the paymentwas received. In some implementations, the invoice analysis platform mayperform the set of actions at the times the invoice analysis platformdetermined to perform the actions after determining that an invoice isoutstanding at the particular time.

Additionally, or alternatively, the invoice analysis platform may updateone or more of the models described herein (e.g., using updated invoicedata, contact data, dispute data, and/or the like). Additionally, oralternatively, the invoice analysis platform may trigger an automatedinvestigation of an invoice (e.g., may trigger a more rigorous analysisof the invoice, such as by requesting input of an explanation of anunderpayment of the invoice). For example, the invoice analysis platformmay send a message to a server device associated with performing theautomated investigation to cause the server device to perform theautomated investigation (e.g., the message may identify an invoice, anentity, and/or the like to be investigated). Additionally, oralternatively, the invoice analysis platform may trigger a manualinvestigation of the invoice (e.g., by sending a message to a userdevice associated with an investigator). For example, the message mayinclude information that identifies an invoice, an entity, and/or thelike to be investigated. Continuing with the previous example, theinvoice analysis platform may provide the message to an electronicaccount associated with an investigator, for display, and/or the like.Additionally, or alternatively, the invoice analysis platform may freezean account associated with an entity until an underpayment is remedied.For example, the invoice analysis platform may send a message to aserver device to block access to an account by an individual, to blockactions on an account, and/or the like. Additionally, or alternatively,the invoice analysis platform may remove, add, or modify a requirementto an order approval process, such as a requirement related to anindividual that needs to authorize provisioning of goods and/or servicesto an entity with a history of underpaying invoices, a timing of thatauthorization (e.g., before or after an order), what needs to bepre-authorized, and/or the like. For example, the invoice analysisplatform may send a message to a server device to configure a setting,related to a requirement of an order approval process, for an entity, aparticular good and/or service, and/or the like.

Additionally, or alternatively, the invoice analysis platform may flagthe invoice based on the prediction. For example, the invoice analysisplatform may flag the invoice as being predicted to be associated with atype of issue and/or for further review based on the prediction byconfiguring a flag, such as a particular value, in an electronic recordassociated with the invoice (e.g., the flag may be displayed with acorresponding invoice when the invoice is provided for display, maycause a device to perform actions based on the flag, and/or the like).Additionally, or alternatively, the invoice analysis platform may flagattributes associated with the invoice. For example, the invoiceanalysis platform may flag an entity, a location, and/or the likeassociated with the invoice. In some implementations, the invoiceanalysis platform may store, in a data structure, information thatidentifies the invoice, whether the invoice is predicted to beassociated with a type of issue (or to not be associated with the typeof issue), and/or the like.

In this way, the invoice analysis platform provides a tool for analyzinghundreds, thousands, or more invoices in real-time or near real-time forprediction of issues that may occur in association with the invoices,for optimization of use of resources for collection of underpaymentsassociated with the invoices, and/or the like. This reduces an amount oftime and/or resources of an organization that need to be used to collectunderpayments for the invoices, thereby improving an efficiency ofcollecting the underpayments. In addition, utilizing the invoiceanalysis platform in this manner offloads tasks related to analyzing theinvoices from conventional computing resources, thereby minimizing acomputing load on the conventional computing resources, minimizingcrashes and/or freezes of the computing resources attributable toanalyzing the invoices, and/or the like. Further, utilizing the invoiceanalysis platform in this manner provides a way to analyze the invoicesin a manner not previously possible, thereby facilitating new insightinto the invoices.

As indicated above, FIGS. 1A-1G are provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIGS. 1A-1G. Although implementation 100 was described in the contextof processing invoices, the implementations apply equally to bills, duespayments, subscriptions, and/or the like. Although some implementationsare described as using various models for classification of data, someimplementations may use the various models for regression-type analyses.

FIG. 2 is a diagram of an example implementation 200 related topredictive issue detection. As shown by reference number 210, an invoiceanalysis platform may receive invoice data, contact data, and/or disputedata in a manner that is the same as or similar to that describedelsewhere herein. For example, the invoice analysis platform may receivethe invoice data, the contact data, and/or the dispute data from aclient device, a server device, a user device, and/or the like. As shownby reference number 220, the invoice analysis platform may determine aprofile for the invoice data, the contact data, and/or the dispute datain a manner that is the same as or similar to that described elsewhereherein. For example, the invoice analysis platform may determine aprofile for the invoice data, the contact data, and/or the dispute databy entity, by invoice, by location, and/or the like.

As shown by reference number 230, the invoice analysis platform maydetermine a set of supervised learning models and/or a set ofunsupervised learning models in a manner that is the same as or similarto that described elsewhere herein. For example, the invoice analysisplatform may determine a neural network, a linear regression model (notshown), an ensemble-based model (not shown), and/or the like as the setof supervised learning models. Additionally, or alternatively, and asanother example, the invoice analysis platform may determine a k-meansclustering model, an isolation forest, and/or the like as the set ofunsupervised learning models. As shown by reference number 240, theinvoice analysis platform may combine the set of supervised learningmodels and the set of unsupervised learning models into a super model ina manner that is the same as or similar to that described elsewhereherein. For example, the invoice analysis platform may combine the setof supervised learning models and the set of unsupervised learningmodels into a random forest model, a neural network, and/or the like.

As shown by reference number 250 the super model may be associated withmaking various predictions for an invoice. For example, the super modelmay be associated with predicting a late payment for the invoice (shownas “Late Payment Prediction”), a late payment after an end of aparticular time (shown as “Late Payment After An End of A ParticularTime Prediction”), such as an end of a month, a length of a late payment(shown as “Length of Late Payment Prediction”), such as a quantity ofdays a payment is expected to be outstanding, and/or the like.

As indicated above, FIG. 2 is provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIG. 2.

FIG. 3 is a diagram of an example implementation 300 related topredictive issue detection.

As shown by reference number 310, a data intake component may beassociated with an invoice analysis platform. In some implementations,the data intake component may receive and/or process invoice data,contact data, dispute data, and/or the like in a manner that is the sameas or similar to that described elsewhere herein. In someimplementations, the data intake component may receive and/or processperiodic data (e.g., data that is updated on a daily, weekly, and/ormonthly basis), real-time (or near real-time) data, and/or the like. Asshown by reference number 320, the invoice analysis platform may includea historical data component. In some implementations, the historicaldata component may populate a historical data storage location (shown byreference number 330) with invoice data related to historical invoices,contact data related to historical contacts, dispute data related tohistorical disputes, data related to historical patterns, and/or thelike.

As shown by reference number 340, the invoice analysis platform mayinclude a model variables component. In some implementations, theinvoice analysis platform may process the data from the historical datastorage location to identify features and/or patterns of the data. Forexample, the model variables component may generate the set ofsupervised learning models and/or the set of unsupervised learningmodels from the data stored in the historical data storage location in amanner that is the same as or similar to that described elsewhereherein. In some implementations, the model variables component may storethe generated set of supervised learning models and/or the generated setof unsupervised learning models in a model storage location (shown byreference number 350). In some implementations, when the invoiceanalysis platform receives invoice-related data for an invoice for whichthe invoice analysis platform is to make a prediction, the invoiceanalysis platform may use the model variables component to process theinvoice-related data in a manner that is the same as or similar to thatdescribed elsewhere herein.

As shown by reference number 360, the invoice analysis platform mayinclude a reporting component. For example, the reporting component maygenerate output that identifies a prediction for an invoice in a mannerthat is the same as or similar to that described elsewhere herein. Asshown by reference number 370, the invoice analysis platform may includea scoring component. For example, the scoring component may determine ascore that indicates a likelihood of a prediction occurring for aninvoice. In some implementations, the scoring component may output ascore to a rules component (shown by reference number 380). In someimplementations, the rules component may generate an invoice list basedon scores for various invoices based on prioritizing the variousinvoices. For example, the invoice list may identify invoicesprioritized for collection of unpaid amounts, for generation ofcollection plans, and/or the like.

As shown by reference number 390, the invoice analysis platform mayinclude a contact optimization component. For example, the contactoptimization component may generate a collection plan for collection ofan unpaid amount associated with an invoice included in the invoicelist, may perform a set of actions associated with the collection plan,and/or the like.

As indicated above, FIG. 3 is provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIG. 3.

FIG. 4 is a diagram of an example environment 400 in which systemsand/or methods described herein may be implemented. As shown in FIG. 4,environment 400 may include a client device 410, a server device 420, aninvoice analysis platform 430 hosted within a cloud computingenvironment 432 that includes a set of computing resources 434, and anetwork 440. Devices of environment 400 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

Client device 410 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith an invoice. For example, client device 410 may include a mobilephone (e.g., a smart phone, a radiotelephone, etc.), a laptop computer,a tablet computer, a handheld computer, a gaming device, a wearablecommunication device (e.g., a smart wristwatch, a pair of smarteyeglasses, etc.), a desktop computer, or a similar type of device. Insome implementations, client device 410 may provide, to invoice analysisplatform 430, an invoice for which a prediction is to be made by invoiceanalysis platform 430, as described elsewhere herein. In someimplementations, a user device, as described elsewhere herein, may bethe same as or similar to client device 410.

Server device 420 includes one or more devices capable of receiving,generating storing, processing, and/or providing information associatedwith an invoice. For example, server device 420 may include a server(e.g., in a data center or a cloud computing environment), a data center(e.g., a multi-server micro datacenter), a workstation computer, avirtual machine (VM) provided in a cloud computing environment, or asimilar type of device. In some implementations, server device 420 mayinclude a communication interface that allows server device 420 toreceive information from and/or transmit information to other devices inenvironment 400. In some implementations, server device 420 may be aphysical device implemented within a housing, such as a chassis. In someimplementations, server device 420 may be a virtual device implementedby one or more computer devices of a cloud computing environment or adata center. In some implementations, server device 420 may provide, toinvoice analysis platform 430, an invoice for which a prediction is tobe made by invoice analysis platform 430, as described elsewhere herein.

Invoice analysis platform 430 includes one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationrelated to invoices. For example, invoice analysis platform 430 mayinclude a cloud server or a group of cloud servers. In someimplementations, invoice analysis platform 430 may be designed to bemodular such that certain software components can be swapped in or outdepending on a particular need. As such, invoice analysis platform 430may be easily and/or quickly reconfigured for different uses.

In some implementations, as shown in FIG. 4, invoice analysis platform430 may be hosted in cloud computing environment 432. Notably, whileimplementations described herein describe invoice analysis platform 430as being hosted in cloud computing environment 432, in someimplementations, invoice analysis platform 430 may be non-cloud-based(i.e., may be implemented outside of a cloud computing environment) ormay be partially cloud-based.

Cloud computing environment 432 includes an environment that hostsinvoice analysis platform 430. Cloud computing environment 432 mayprovide computation, software, data access, storage, and/or otherservices that do not require end-user knowledge of a physical locationand configuration of a system and/or a device that hosts invoiceanalysis platform 430. As shown, cloud computing environment 432 mayinclude a group of computing resources 434 (referred to collectively as“computing resources 434” and individually as “computing resource 434”).

Computing resource 434 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource434 may host invoice analysis platform 430. The cloud resources mayinclude compute instances executing in computing resource 434, storagedevices provided in computing resource 434, data transfer devicesprovided by computing resource 434, etc. In some implementations,computing resource 434 may communicate with other computing resources434 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 4, computing resource 434 may include a groupof cloud resources, such as one or more applications (“APPs”) 434-1, oneor more virtual machines (“VMs”) 434-2, one or more virtualized storages(“VSs”) 434-3, or one or more hypervisors (“HYPs”) 434-4.

Application 434-1 includes one or more software applications that may beprovided to or accessed by one or more devices of environment 400.Application 434-1 may eliminate a need to install and execute thesoftware applications on devices of environment 400. For example,application 434-1 may include software associated with invoice analysisplatform 430 and/or any other software capable of being provided viacloud computing environment 432. In some implementations, oneapplication 434-1 may send/receive information to/from one or more otherapplications 434-1, via virtual machine 434-2. In some implementations,application 434-1 may include a software application associated with oneor more databases and/or operating systems. For example, application434-1 may include an enterprise application, a functional application,an analytics application, and/or the like.

Virtual machine 434-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 434-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 434-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 434-2 may execute on behalf of a user(e.g., a user of client device 410), and may manage infrastructure ofcloud computing environment 432, such as data management,synchronization, or long-duration data transfers.

Virtualized storage 434-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 434. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 434-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 434.Hypervisor 434-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 440 includes one or more wired and/or wireless networks. Forexample, network 440 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, etc.), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the Public Switched TelephoneNetwork (PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 4 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 4. Furthermore, two or more devices shown in FIG. 4 may beimplemented within a single device, or a single device shown in FIG. 4may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 400 may perform one or more functions described as beingperformed by another set of devices of environment 400.

FIG. 5 is a diagram of example components of a device 500. Device 500may correspond to client device 410, server device 420, invoice analysisplatform 430, and/or computing resource 434. In some implementations,client device 410, server device 420, invoice analysis platform 430,and/or computing resource 434 may include one or more devices 500 and/orone or more components of device 500. As shown in FIG. 5, device 500 mayinclude a bus 510, a processor 520, a memory 530, a storage component540, an input component 550, an output component 560, and acommunication interface 570.

Bus 510 includes a component that permits communication among thecomponents of device 500. Processor 520 is implemented in hardware,firmware, or a combination of hardware and software. Processor 520 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 520includes one or more processors capable of being programmed to perform afunction. Memory 530 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 520.

Storage component 540 stores information and/or software related to theoperation and use of device 500. For example, storage component 540 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 550 includes a component that permits device 500 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 550 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 560 includes a component that providesoutput information from device 500 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 570 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 500 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 570 may permit device 500to receive information from another device and/or provide information toanother device. For example, communication interface 570 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 500 may perform one or more processes described herein. Device500 may perform these processes based on to processor 520 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 530 and/or storage component 540. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 530 and/or storagecomponent 540 from another computer-readable medium or from anotherdevice via communication interface 570. When executed, softwareinstructions stored in memory 530 and/or storage component 540 may causeprocessor 520 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 5 are provided asan example. In practice, device 500 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 5. Additionally, or alternatively, aset of components (e.g., one or more components) of device 500 mayperform one or more functions described as being performed by anotherset of components of device 500.

FIG. 6 is a flow chart of an example process 600 for predictive issuedetection. In some implementations, one or more process blocks of FIG. 6may be performed by an invoice analysis platform (e.g., invoice analysisplatform 430). In some implementations, one or more process blocks ofFIG. 6 may be performed by another device or a group of devices separatefrom or including the invoice analysis platform, such as a client device(e.g., client device 410), a server device (e.g., server device 420),and a computing resource (e.g., computing resource 434).

As shown in FIG. 6, process 600 may include receiving data thatincludes: invoice data related to historical invoices from anorganization, contact data related to historical contacts between theorganization and various entities for the historical invoices, anddispute data related to historical disputes between the organization andthe various entities for the historical invoices (block 610). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, input component 550, communication interface 570,and/or the like) may receive data that includes: invoice data related tohistorical invoices from an organization, contact data related tohistorical contacts between the organization and various entities forthe historical invoices, and dispute data related to historical disputesbetween the organization and the various entities for the historicalinvoices, in a manner that is the same as or similar to that describedelsewhere herein.

As further shown in FIG. 6, process 600 may include determining aprofile for the data after receiving the data, wherein the profileincludes a set of groupings of the data by a set of attributes includedin the data (block 620). For example, the invoice analysis platform(e.g., using computing resource 434, processor 520, and/or the like) maydetermine a profile for the data after receiving the data, in a mannerthat is the same as or similar to that described elsewhere herein. Insome implementations, the profile includes a set of groupings of thedata by a set of attributes included in the data.

As further shown in FIG. 6, process 600 may include determining, usingthe profile, a set of supervised learning models for the historicalinvoices based on one or more of: the historical contacts, thehistorical disputes, or historical patterns related to the historicalinvoices, wherein the set of supervised learning models is associatedwith training a super model to make a prediction for an invoice in acontext of the one or more of the historical contacts, the historicaldisputes, the historical invoices, or the historical patterns (block630). For example, the invoice analysis platform (e.g., using computingresource 434, processor 520, and/or the like) may determine, using theprofile, a set of supervised learning models for the historical invoicesbased on one or more of: the historical contacts, the historicaldisputes, or historical patterns related to the historical invoices, ina manner that is the same as or similar to that described elsewhereherein. In some implementations, the set of supervised learning modelsis associated with training a super model to make a prediction for aninvoice in a context of the one or more of the historical contacts, thehistorical disputes, the historical invoices, or the historicalpatterns.

As further shown in FIG. 6, process 600 may include determining, usingthe profile, a set of unsupervised learning models for the historicalinvoices independent of the one or more of the historical contacts, thehistorical disputes, or the historical patterns, wherein the set ofunsupervised learning models is associated with training the super modelto make the prediction for the invoice independent of the context of theone or more of the historical contacts, the historical disputes, thehistorical invoices, or the historical patterns (block 640). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, and/or the like) may determine, using the profile, aset of unsupervised learning models for the historical invoicesindependent of the one or more of the historical contacts, thehistorical disputes, or the historical patterns, in a manner that is thesame as or similar to that described elsewhere herein. In someimplementations, the set of unsupervised learning models is associatedwith training the super model to make the prediction for the invoiceindependent of the context of the one or more of the historicalcontacts, the historical disputes, or the historical patterns.

As further shown in FIG. 6, process 600 may include determining,utilizing the super model, the prediction for the invoice after thesuper model is trained using the set of supervised learning models andthe set of unsupervised learning models, wherein the predictionindicates a likelihood of at least one type of issue being associatedwith the invoice (block 650). For example, the invoice analysis platform(e.g., using computing resource 434, processor 520, and/or the like) maydetermine, utilizing the super model, the prediction for the invoiceafter the super model is trained using the set of supervised learningmodels and the set of unsupervised learning models, in a manner that isthe same as or similar to that described elsewhere herein. In someimplementations, the prediction indicates a likelihood of at least onetype of issue being associated with the invoice.

As further shown in FIG. 6, process 600 may include performing one ormore actions based on the prediction (block 660). For example, theinvoice analysis platform (e.g., using processor 520, memory 530,storage component 540, output component 560, communication interface570, and/or the like) may perform one or more actions based on theprediction, in a manner that is the same as or similar to that describedelsewhere herein.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described hereinand/or in connection with one or more other processes described herein.

In some implementations, the invoice analysis platform may determine theset of supervised learning models based on a set of models thatincludes: a linear regression model, an ensemble-based model, or aneural network. In some implementations, the invoice analysis platformmay determine the set of unsupervised learning models based on a set ofmodels that includes: a k-means clustering model, a kernel densityestimation (KDE) model, or an isolation forest.

In some implementations, the invoice analysis platform may determine theprofile based on respective geographic locations associated with thevarious entities or the historical invoices. In some implementations,the set of supervised learning models identifies accurately predictedhistorical invoices and inaccurately predicted historical invoices.

In some implementations, the set of unsupervised learning modelsidentifies subsets of the historical invoices that correspond toparticular patterns of the data. In some implementations, the invoiceanalysis platform may flag the invoice as being predicted to beassociated with the at least one type of issue or to not be associatedwith the at least one type of issue based on the prediction; andstoring, in a data structure, information that identifies the invoiceand whether the invoice is predicted to be associated with the at leastone type of issue or to not be associated with the at least one type ofissue.

Although FIG. 6 shows example blocks of process 600, in someimplementations process 600 may include additional blocks, fewer blocks,different blocks, or differently arranged blocks than those depicted inFIG. 6. Additionally, or alternatively, two or more of the blocks ofprocess 600 may be performed in parallel.

FIG. 7 is a flow chart of an example process 700 for predictive issuedetection. In some implementations, one or more process blocks of FIG. 7may be performed by an invoice analysis platform (e.g., invoice analysisplatform 430). In some implementations, one or more process blocks ofFIG. 7 may be performed by another device or a group of devices separatefrom or including the invoice analysis platform, such as a client device(e.g., client device 410), a server device (e.g., server device 420),and a computing resource (e.g., computing resource 434).

As shown in FIG. 7, process 700 may include receiving data that isrelated to training a super model to determine a prediction of at leastone type of issue being associated with an invoice (block 710). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, input component 550, communication interface 570,and/or the like) may receive data that is related to training a supermodel to determine a prediction of at least one type of issue beingassociated with an invoice, in a manner that is the same as or similarto that described elsewhere herein.

As further shown in FIG. 7, process 700 may include determining aprofile for the data after receiving the data, wherein the profileincludes a set of groupings of the data by a set of attributes includedin the data (block 720). For example, the invoice analysis platform(e.g., using computing resource 434, processor 520, and/or the like) maydetermine a profile for the data after receiving the data, in a mannerthat is the same as or similar to that described elsewhere herein. Insome implementations, the profile includes a set of groupings of thedata by a set of attributes included in the data.

As further shown in FIG. 7, process 700 may include determining, usingthe profile, a set of supervised learning models for historical invoicesbased on at least one of: historical patterns related to the historicalinvoices, historical contacts related to historical issues associatedwith the historical invoices, or historical disputes related to thehistorical invoices, wherein the set of supervised learning models isassociated with training the super model to make the prediction for theinvoice in a context of the at least one of the historical patterns, thehistorical contacts, or the historical disputes (block 730). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, and/or the like) may determine, using the profile, aset of supervised learning models for historical invoices based on atleast one of: historical patterns related to the historical invoices,historical contacts related to historical issues associated with thehistorical invoices, or historical disputes related to the historicalinvoices, in a manner that is the same as or similar to that describedelsewhere herein. In some implementations, the set of supervisedlearning models is associated with training the super model to make theprediction for the invoice in a context of the at least one of thehistorical patterns, the historical contacts, or the historicaldisputes.

As further shown in FIG. 7, process 700 may include determining, usingthe profile, a set of unsupervised learning models for the historicalinvoices independent of the at least one of the historical patterns, thehistorical contacts, or the historical disputes, wherein the set ofunsupervised learning models is associated with training the super modelto make the prediction for the invoice independent of the at least oneof the context of the historical patterns, the historical invoices, thehistorical contacts, or the historical disputes (block 740). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, and/or the like) may determine, using the profile, aset of unsupervised learning models for the historical invoicesindependent of the at least one of the historical patterns, thehistorical contacts, or the historical disputes, in a manner that is thesame as or similar to that described elsewhere herein. In someimplementations, the set of unsupervised learning models is associatedwith training the super model to make the prediction for the invoiceindependent of the at least one of the context of the historicalpatterns, the historical invoices, the historical contacts, or thehistorical disputes.

As further shown in FIG. 7, process 700 may include generating,utilizing the super model, the prediction for the invoice by processinginvoice-related data after the super model is trained using the set ofsupervised learning models and the set of unsupervised learning models,wherein the at least one type of issue includes at least one of: a latepayment associated with the invoice, the late payment after a particulartime, or an amount of time for the late payment (block 750). Forexample, the invoice analysis platform (e.g., using computing resource434, processor 520, and/or the like) may generate, utilizing the supermodel, the prediction for the invoice by processing invoice-related dataafter the super model is trained using the set of supervised learningmodels and the set of unsupervised learning models, in a manner that isthe same as or similar to that described elsewhere herein. In someimplementations, the at least one type of issue includes at least oneof: a late payment associated with the invoice, the late payment after aparticular time, or an amount of time for the late payment.

As further shown in FIG. 7, process 700 may include ranking the invoicerelative to one or more other invoices based on being predicted to beassociated with the at least one type of issue based on the prediction(block 760). For example, the invoice analysis platform (e.g., usingcomputing resource 434, processor 520, output component 560,communication interface 570, and/or the like) may rank the invoicerelative to one or more other invoices based on being predicted to beassociated with the at least one type of issue based on the prediction,in a manner that is the same as or similar to that described elsewhereherein.

Process 700 may include additional implementations, such as any singleimplementation or any combination of implementations described hereinand/or in connection with one or more other processes described herein.

In some implementations, the invoice analysis platform may train thesuper model, utilizing the set of supervised learning models and the setof unsupervised learning models and prior to generating the prediction,to identify a likelihood of at least one type of issue being associatedwith the invoice. In some implementations, the invoice analysis platformmay process, utilizing the super model, the invoice-related data topredict the likelihood of the at least one type of issue beingassociated with the invoice after training the super model.

In some implementations, the invoice analysis platform may determine,utilizing the super model, a score for the invoice after training thesuper model, wherein the score indicates the likelihood of the at leastone type of issue being associated with the invoice; and may determine apriority for the invoice relative to one or more other invoices based onranking the invoice. In some implementations, the invoice analysisplatform may generate a collection plan for the invoice based on apriority for the invoice or at least one type issue predicted to beassociated with the invoice; and may output information relating to thecollection plan via a display after generating the collection plan.

In some implementations, the invoice analysis platform may determine,after ranking the invoice, a set of actions to perform and respectivetimes at which to perform the set of actions based on the predictionrelated to the invoice, wherein the set of actions includes at least oneof: sending a message to another device associated with an entityassociated with the invoice, placing a robotic telephone call to atelephone associated with the entity, populating an electronic queueassociated with a collection agent with information related to theinvoice, or dispatching the collection agent to a location associatedwith the entity. In some implementations, the invoice analysis platformmay determine that the invoice is outstanding at the particular timeafter determining the set of actions and the respective times at whichto perform the set of actions; and may perform the set of actions at therespective times after determining that the invoice is outstanding atthe particular time.

Although FIG. 7 shows example blocks of process 700, in someimplementations process 700 may include additional blocks, fewer blocks,different blocks, or differently arranged blocks than those depicted inFIG. 7. Additionally, or alternatively, two or more of the blocks ofprocess 700 may be performed in parallel.

FIG. 8 is a flow chart of an example process 800 for predictive issuedetection. In some implementations, one or more process blocks of FIG. 8may be performed by an invoice analysis platform (e.g., invoice analysisplatform 430). In some implementations, one or more process blocks ofFIG. 8 may be performed by another device or a group of devices separatefrom or including the invoice analysis platform, such as a client device(e.g., client device 410), a server device (e.g., server device 420),and a computing resource (e.g., computing resource 434).

As shown in FIG. 8, process 800 may include receiving invoice-relateddata that is to be processed by a super model to determine a predictionof at least one type of issue being associated with an invoice, whereinthe super model has been trained on data related to: historical invoicesfrom an organization, historical contacts between the organization andvarious entities for the historical invoices, and historical disputesbetween the organization and the various entities for the historicalinvoices (block 810). For example, the invoice analysis platform (e.g.,using computing resource 434, processor 520, input component 550,communication interface 570, and/or the like) may receiveinvoice-related data that is to be processed by a super model todetermine a prediction of at least one type of issue being associatedwith an invoice, in a manner that is the same as or similar to thatdescribed elsewhere herein. In some implementations, the super model hasbeen trained on data related to: historical invoices from anorganization, historical contacts between the organization and variousentities for the historical invoices, and historical disputes betweenthe organization and the various entities for the historical invoices.

As further shown in FIG. 8, process 800 may include processing, using aset of supervised learning models, the invoice-related data afterreceiving the invoice-related data, wherein the set of supervisedlearning models has been trained to make the prediction for the invoicein a context of one or more of the historical contacts, the historicaldisputes, or historical patterns (block 820). For example, the invoiceanalysis platform (e.g., using computing resource 434, processor 520,and/or the like) may process, using a set of supervised learning models,the invoice-related data after receiving the invoice-related data, in amanner that is the same as or similar to that described elsewhereherein. In some implementations, the set of supervised learning modelshas been trained to make the prediction for the invoice in a context ofone or more of the historical contacts, the historical disputes, orhistorical patterns.

As further shown in FIG. 8, process 800 may include processing, using aset of unsupervised learning models, the invoice-related data afterprocessing the set of supervised learning models, wherein the set ofunsupervised learning models has been trained to make the prediction forthe invoice independent of the context of the one or more of thehistorical patterns, the historical contacts, or the historical disputes(block 830). For example, the invoice analysis platform (e.g., usingcomputing resource 434, processor 520, and/or the like) may process,using a set of unsupervised learning models, the invoice-related dataafter processing the set of supervised learning models, in a manner thatis the same as or similar to that described elsewhere herein. In someimplementations, the set of unsupervised learning models has beentrained to make the prediction for the invoice independent of thecontext of the one or more of the historical patterns, the historicalcontacts, or the historical disputes.

As further shown in FIG. 8, process 800 may include processing, usingthe super model, output from the set of supervised learning models andthe set of unsupervised learning models to make the prediction of the atleast one type issue being associated with the invoice after processingthe invoice-related data using the set of supervised learning models andthe set of unsupervised learning models (block 840). For example, theinvoice analysis platform (e.g., using computing resource 434, processor520, and/or the like) may process, using the super model, output fromthe set of supervised learning models and the set of unsupervisedlearning models to make the prediction of the at least one type issuebeing associated with the invoice after processing the invoice-relateddata using the set of supervised learning models and the set ofunsupervised learning models, in a manner that tis the same as orsimilar to that described elsewhere herein.

As further shown in FIG. 8, process 800 may include performing one ormore actions based on the prediction (block 850). For example, theinvoice analysis platform (e.g., using computing resource 434, processor520, and/or the like) may perform one or more actions based on theprediction, in a manner that is the same as or similar to that describedelsewhere herein.

Process 800 may include additional implementations, such as any singleimplementation or any combination of implementations described hereinand/or in connection with one or more other processes described herein.

In some implementations, the invoice analysis platform may train the setof supervised learning models and the set of unsupervised learningmodels using the data prior to receiving the invoice-related data; andmay combine the set of supervised learning models and the set ofunsupervised learning models into the super model after training the setof supervised learning models and the set of unsupervised learningmodels. In some implementations, the invoice analysis platform maycombine the set of supervised learning models and the set ofunsupervised learning models into at least one of: a random forestmodel, or a neural network, wherein the super model comprises the atleast one of the random forest model or the neural network.

In some implementations, the invoice analysis platform may send amessage to a device after processing the output from the set ofsupervised learning models and the set of unsupervised learning models,wherein the message includes information that identifies the invoice orthe prediction. In some implementations, the invoice analysis platformmay determine, after processing the output from the set of supervisedlearning models and the set of unsupervised learning models, that theinvoice is associated with the at least one type of issue or one or moreother types of issues; and may perform the one or more actions based onthe at least one type of issue or the one or more other types of issuesafter determining that the invoice is associated with the at least onetype of issue or the one or more other types of issues. In someimplementations, the super model is associated with at least one of:predicting a late payment related to the invoice, predicting the latepayment related to the invoice after a particular time, or predicting anamount of time payment related to the invoice will be outstanding.

Although FIG. 8 shows example blocks of process 800, in someimplementations process 800 may include additional blocks, fewer blocks,different blocks, or differently arranged blocks than those depicted inFIG. 8. Additionally, or alternatively, two or more of the blocks ofprocess 800 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, or the like.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: receiving, by a device,data that includes: invoice data related to historical invoices from anorganization, contact data related to historical contacts between theorganization and various entities for the historical invoices, anddispute data related to historical disputes between the organization andthe various entities for the historical invoices; determining, by thedevice, a profile for the data after receiving the data, wherein theprofile includes a set of groupings of the data by a set of attributesincluded in the data; determining, by the device and using the profile,a set of supervised learning models for the historical invoices based onone or more of: the historical contacts, the historical disputes, orhistorical patterns related to the historical invoices, wherein the setof supervised learning models is associated with training a super modelto make a prediction for an invoice in a context of the one or more ofthe historical contacts, the historical disputes, the historicalinvoices, or the historical patterns; determining, by the device andusing the profile, a set of unsupervised learning models for thehistorical invoices independent of the one or more of the historicalcontacts, the historical disputes, or the historical patterns, whereinthe set of unsupervised learning models is associated with training thesuper model to make the prediction for the invoice independent of thecontext of the one or more of the historical contacts, the historicaldisputes, the historical invoices, or the historical patterns;determining, by the device and utilizing the super model, the predictionfor the invoice after the super model is trained using the set ofsupervised learning models and the set of unsupervised learning models,wherein the prediction indicates a likelihood of at least one type ofissue being associated with the invoice; and performing, by the device,one or more actions based on the prediction.
 2. The method of claim 1,wherein determining the set of supervised learning models is based on aset of models that includes: a linear regression model, anensemble-based model, or a neural network.
 3. The method of claim 1,wherein determining the set of unsupervised learning models is based ona set of models that includes: a k-means clustering model, a kerneldensity estimation (KDE) model, or an isolation forest.
 4. The method ofclaim 1, wherein determining the profile is based on respectivegeographic locations associated with the various entities or thehistorical invoices.
 5. The method of claim 1, wherein the set ofsupervised learning models identifies accurately predicted historicalinvoices and inaccurately predicted historical invoices.
 6. The methodof claim 1, wherein the set of unsupervised learning models identifiessubsets of the historical invoices that correspond to particularpatterns of the data.
 7. The method of claim 1, wherein performing theone or more actions comprises: flagging the invoice as being predictedto be associated with the at least one type of issue or to not beassociated with the at least one type of issue based on the prediction;and storing, in a data structure, information that identifies theinvoice and whether the invoice is predicted to be associated with theat least one type of issue or to not be associated with the at least onetype of issue.
 8. A device, comprising: one or more memories; and one ormore processors, communicatively coupled to the one or more memories,to: receive data that is related to training a super model to determinea prediction of at least one type of issue being associated with aninvoice; determine a profile for the data after receiving the data,wherein the profile includes a set of groupings of the data by a set ofattributes included in the data; determine, using the profile, a set ofsupervised learning models for historical invoices based on at least oneof: historical patterns related to the historical invoices, historicalcontacts related to historical issues associated with the historicalinvoices, or historical disputes related to the historical invoices,wherein the set of supervised learning models is associated withtraining the super model to make the prediction for the invoice in acontext of the at least one of the historical patterns, the historicalcontacts, or the historical disputes; determine, using the profile, aset of unsupervised learning models for the historical invoicesindependent of the at least one of the historical patterns, thehistorical contacts, or the historical disputes, wherein the set ofunsupervised learning models is associated with training the super modelto make the prediction for the invoice independent of the at least oneof the context of the historical patterns, the historical invoices, thehistorical contacts, or the historical disputes; generate, utilizing thesuper model, the prediction for the invoice by processinginvoice-related data after the super model is trained using the set ofsupervised learning models and the set of unsupervised learning models,wherein the at least one type of issue includes at least one of: a latepayment associated with the invoice, the late payment after a particulartime, or an amount of time for the late payment; and rank the invoicerelative to one or more other invoices based on being predicted to beassociated with the at least one type of issue based on the prediction.9. The device of claim 8, wherein the one or more processors are furtherto: train the super model, utilizing the set of supervised learningmodels and the set of unsupervised learning models and prior togenerating the prediction, to identify a likelihood of at least one typeof issue being associated with the invoice.
 10. The device of claim 9,wherein the one or more processors, when generating the prediction, areto: process, utilizing the super model, the invoice-related data topredict the likelihood of the at least one type of issue beingassociated with the invoice after training the super model.
 11. Thedevice of claim 10, wherein the one or more processors, when generatingthe prediction, are to: determine, utilizing the super model, a scorefor the invoice after training the super model, wherein the scoreindicates the likelihood of the at least one type of issue beingassociated with the invoice; and wherein the one or more processors arefurther to: determine a priority for the invoice relative to one or moreother invoices based on ranking the invoice.
 12. The device of claim 8,wherein the one or more processors are further to: generate a collectionplan for the invoice based on a priority for the invoice or the at leastone type issue predicted to be associated with the invoice; and outputinformation relating to the collection plan via a display aftergenerating the collection plan.
 13. The device of claim 8, wherein theone or more processors are further to: determine, after ranking theinvoice, a set of actions to perform and respective times at which toperform the set of actions based on the prediction related to theinvoice, wherein the set of actions includes at least one of: sending amessage to another device associated with an entity associated with theinvoice, placing a robotic telephone call to a telephone associated withthe entity, populating an electronic queue associated with a collectionagent with information related to the invoice, or dispatching thecollection agent to a location associated with the entity.
 14. Thedevice of claim 13, wherein the one or more processors are further to:determine that the invoice is outstanding at the particular time afterdetermining the set of actions and the respective times at which toperform the set of actions; and perform the set of actions at therespective times after determining that the invoice is outstanding atthe particular time.
 15. A non-transitory computer-readable mediumstoring instructions, the instructions comprising: one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: receive invoice-related data that is to beprocessed by a super model to determine a prediction of at least onetype of issue being associated with an invoice, wherein the super modelhas been trained on data related to: historical invoices from anorganization, historical contacts between the organization and variousentities for the historical invoices, and historical disputes betweenthe organization and the various entities for the historical invoices;process, using a set of supervised learning models, the invoice-relateddata after receiving the invoice-related data, wherein the set ofsupervised learning models has been trained to make the prediction forthe invoice in a context of one or more of the historical contacts, thehistorical disputes, or historical patterns; process, using a set ofunsupervised learning models, the invoice-related data after processingthe set of supervised learning models, wherein the set of unsupervisedlearning models has been trained to make the prediction for the invoiceindependent of the context of the one or more of the historicalpatterns, the historical contacts, or the historical disputes; process,using the super model, output from the set of supervised learning modelsand the set of unsupervised learning models to make the prediction ofthe at least one type issue being associated with the invoice afterprocessing the invoice-related data using the set of supervised learningmodels and the set of unsupervised learning models; and perform one ormore actions based on the prediction.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: train the set of supervised learningmodels and the set of unsupervised learning models using the data priorto receiving the invoice-related data; and combine the set of supervisedlearning models and the set of unsupervised learning models into thesuper model after training the set of supervised learning models and theset of unsupervised learning models.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the one or moreinstructions, that cause the one or more processors to combine the setof supervised learning models and the set of unsupervised learningmodels into the super model, cause the one or more processors to:combine the set of supervised learning models and the set ofunsupervised learning models into at least one of: a random forestmodel, or a neural network, wherein the super model comprises the atleast one of the random forest model or the neural network.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the one or more processors to perform theone or more actions, cause the one or more processors to: send a messageto a device after processing the output from the set of supervisedlearning models and the set of unsupervised learning models, wherein themessage includes information that identifies the invoice or theprediction.
 19. The non-transitory computer-readable medium of claim 15,wherein the one or more instructions, when executed by the one or moreprocessors, further cause the one or more processors to: determine,after processing the output from the set of supervised learning modelsand the set of unsupervised learning models, that the invoice isassociated with the at least one type of issue or one or more othertypes of issues; and wherein the one or more instructions, that causethe one or more processors to perform the one or more actions, cause theone or more processors to: perform the one or more actions based on theat least one type of issue or the one or more other types of issuesafter determining that the invoice is associated with the at least onetype of issue or the one or more other types of issues.
 20. Thenon-transitory computer-readable medium of claim 19, wherein the supermodel is associated with at least one of: predicting a late paymentrelated to the invoice, predicting the late payment related to theinvoice after a particular time, or predicting an amount of time paymentrelated to the invoice will be outstanding.